Claude Shannon's 1948 paper introduced information entropy, a measure of uncertainty or disorder in a system. Entropy helps quantify unpredictability, with higher entropy indicating more unknowns. This concept is useful in various fields, including finance. For instance, examining the entropy of price histories may reveal trade signals. Using Shannon's entropy in trading involves comparing the entropy of rising and falling price bars to identify potential buy or sell signals.

The Decision Forest class helps in implementing Shannon's entropy signal within the MQL5 trading platform. Decision trees, a fundamental part of machine learning, classify data based on attributes. In trading, attributes can include price direction or other key indicators. By employing random forests, a collection of decision trees, trading strategies can benefit from diversifie...
#MQL5 #MT5 #Entropy #AlgoTrading

Read more...